标题： 基于聚类的随机森林可视化与解释 显示英文标题

标题： Cluster-Based Random Forest Visualization and Interpretation

摘要： 随机森林是一种用于自动分类数据集的机器学习方法，由大量决策树组成。 虽然这些随机森林通常比单个决策树具有更高的性能并能更好地泛化，但它们也更难以解释。 本文提出了一种可视化方法和系统，以提高随机森林的可解释性。 我们对相似的树木进行聚类，使用户能够在不需要详细分析每个单独的决策树或解释整个森林的过度简化的总结的情况下，理解模型的一般表现。 为了有意义地对决策树进行聚类，我们引入了一种新的距离度量，该度量考虑了成对决策树的决策规则以及预测结果。 我们还提出了两种新的可视化方法，用于可视化聚类和单独的决策树：(1) 特征图，用于可视化决策树中特征的拓扑位置，以及(2) 规则图，用于可视化决策树的决策规则。 我们通过一个关于“玻璃”数据集的案例研究以及一个小规模的用户研究来证明我们方法的有效性，该数据集是一个相对复杂的标准机器学习数据集。 显示英文摘要

摘要： Random forests are a machine learning method used to automatically classify datasets and consist of a multitude of decision trees. While these random forests often have higher performance and generalize better than a single decision tree, they are also harder to interpret. This paper presents a visualization method and system to increase interpretability of random forests. We cluster similar trees which enables users to interpret how the model performs in general without needing to analyze each individual decision tree in detail, or interpret an oversimplified summary of the full forest. To meaningfully cluster the decision trees, we introduce a new distance metric that takes into account both the decision rules as well as the predictions of a pair of decision trees. We also propose two new visualization methods that visualize both clustered and individual decision trees: (1) The Feature Plot, which visualizes the topological position of features in the decision trees, and (2) the Rule Plot, which visualizes the decision rules of the decision trees. We demonstrate the efficacy of our approach through a case study on the "Glass" dataset, which is a relatively complex standard machine learning dataset, as well as a small user study.